The invention relates to a method of estimating probabilities of occurrence of speech vocabulary elements in a speech recognition system.
In speech recognition systems based on static models, an acoustic speech modeling and a linguistic speech modeling are used. The invention relates to the field of linguistic speech modeling.
It is known to determine probabilities of occurrence of elements of a speech vocabulary by a linear combination of different M-gram probabilities of these elements. It is known from R. Kneser, V. Steinbiss, "On the dynamic adaptation of stochastic language models", Proc. ICASSP, pp. 586-589, 1993 that, for forming probabilities of occurrence of bigram vocabulary elements, a plurality of probabilities of occurrence determined for different training vocabulary corpuses of these bigram vocabulary elements is combined linearly so as to form probabilities of occurrence of these elements.
It is also known from R. Kneser, J. Peters and D. Klakow, "Language Model Adaptation using Dynamic Marginals", (see formulas (8) and (9)), EUROSPEECH, pp. 1971-1974, 1997 that, in the estimation of the probability of occurrence of a speech vocabulary element, an M-gram probability with M&gt;1 estimated by means of a first training vocabulary corpus for the speech vocabulary element is multiplied by a quotient raised to the power by means of an optimized parameter value, which optimized parameter value is determined by means of the GIS algorithm (Generalized Iterative Scaling), and a unigram probability of the element estimated by means of a second training vocabulary corpus serves as a dividend of the quotient, and a unigram probability of the element estimated by means of the first training vocabulary corpus serves as a divisor of the quotient. The error rate and the perplexity of a speech recognition system can be reduced with this formulation.